Medical Large Language Model

Medical Large Language Models (mLLMs) aim to leverage the power of LLMs for healthcare applications, focusing on tasks like diagnosis, treatment recommendations, and report generation. Current research emphasizes improving mLLM performance through techniques like reinforcement learning, multi-modal integration (combining text and images), and advanced fine-tuning strategies including parameter-efficient methods, often building upon existing large language model architectures such as Llama. The development of comprehensive benchmarks and large, high-quality datasets is crucial for evaluating and improving mLLM capabilities, ultimately aiming to enhance clinical workflows and patient care.